{"id":17153178,"url":"https://github.com/axect/noisy_candle","last_synced_at":"2025-06-25T15:07:14.589Z","repository":{"id":230018432,"uuid":"778164527","full_name":"Axect/Noisy_Candle","owner":"Axect","description":"A Rust project showcasing regression on noisy data using machine learning libraries.","archived":false,"fork":false,"pushed_at":"2024-03-30T14:41:29.000Z","size":1086,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-06-19T03:05:52.080Z","etag":null,"topics":["machine-learning","rust"],"latest_commit_sha":null,"homepage":"","language":"Rust","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Axect.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2024-03-27T07:48:44.000Z","updated_at":"2024-03-27T11:12:32.000Z","dependencies_parsed_at":"2024-03-27T12:29:44.059Z","dependency_job_id":null,"html_url":"https://github.com/Axect/Noisy_Candle","commit_stats":null,"previous_names":["axect/noisy_candle"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Axect/Noisy_Candle","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Axect%2FNoisy_Candle","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Axect%2FNoisy_Candle/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Axect%2FNoisy_Candle/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Axect%2FNoisy_Candle/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Axect","download_url":"https://codeload.github.com/Axect/Noisy_Candle/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Axect%2FNoisy_Candle/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261896999,"owners_count":23226647,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","rust"],"created_at":"2024-10-14T21:45:30.620Z","updated_at":"2025-06-25T15:07:14.563Z","avatar_url":"https://github.com/Axect.png","language":"Rust","readme":"# Noisy Regression with Candle\n\nThis project demonstrates how to perform regression on noisy data using various Rust libraries for machine learning. The main libraries used in this project are:\n\n- `peroxide`: A library for data generation, preprocessing and visualization\n- `candle`: A library for defining and training machine learning models\n- `rayon`: A library for parallel processing\n- `indicatif`: A library for progress tracking and visualization\n\n## Key Features\n\n1. Dataset Generation and Preprocessing\n   - Generate noisy data using the `peroxide` library.\n   - Split the data into train, validation, and test sets.\n   - Scale the data using scalers such as `MinMaxScaler`, `StandardScaler`, and `RobustScaler`.\n\n2. Model Definition and Training\n   - Define an MLP (Multi-Layer Perceptron) model using the `candle` library.\n   - Train the model using the Adam optimizer.\n   - Track the training progress using the `indicatif` library.\n\n3. Model Evaluation and Visualization\n   - Evaluate the trained model on the test dataset and calculate the MSE.\n   - Visualize the predictions using `plot` feature of `peroxide` library.\n\n## Prerequisites\n\n- Rust\n- Python\n- Required python libraries\n  - `matplotlib`\n  - `scienceplots` (optional)\n\n## Usage\n\nJust run the Rust code:\n\n```sh\ncargo run --release\n```\n\n## Results\n\n- The training and validation losses are printed during the training process.\n- The MSE on the test dataset is displayed after the model evaluation.\n- The predicted results are visualized and saved to the `test_plot.png` file.\n  ![test_plot.png](test_plot.png)\n\n## LICENSE\n\nThis project is licensed under the MIT license.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faxect%2Fnoisy_candle","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faxect%2Fnoisy_candle","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faxect%2Fnoisy_candle/lists"}